1. Field of the Invention
The present invention relates to learning control systems and learning control methods using reinforcement learning.
2. Background Art
Reinforcement learning is known as a method of learning for a mechanical or computational system in which learning of action rules of agents including persons and animals is performed and the mechanical or computational system's control rules are adapted to achieve its own target. For example, Japanese Patent No. 3465236 can be referred to. Reinforcement learning is used in a robot which takes actions in an unknown environment, for example. However, there exists a problem that learning efficiency of reinforcement learning is low and therefore it takes long for learning.
On the other hand, multi-agent reinforcement learning (MARL) in which kinds of actions to be taken by agents are previously determined before learning is performed has been developed. The previous determination of the kinds of actions improves learning efficiency. For example, Japanese Patent Application Laid Open No. 2000-20494 can be referred to. However, to utilize MARL, the kinds of actions to be taken by agents have to be previously known, and therefore MARL cannot be performed based only on the information obtained by the observation of the states of the agents. Thus, MARL cannot be applied to the cases in which the kinds of actions to be taken by agents cannot be previously determined due to lack of prior knowledge. Accordingly, MARL can hardly be applied to real environments including agents.
Thus, there is a need for a highly efficient reinforcement learning system and a highly efficient reinforcement learning method which can be applied to the real environments including agents without prior knowledge.